Directed Graphs

Not all backbone methods support directed graphs. This guide explains which methods work with directed graphs and how their behavior differs.

Supported methods

The following methods support both directed and undirected graphs:

Statistical methods (all 5):

disparity_filter(), noise_corrected_filter(), marginal_likelihood_filter(), ecm_filter(), lans_filter()

Structural (2 of 10):

global_threshold_filter(), strongest_n_ties()

Proximity methods (all 12):

All proximity scoring functions work with directed graphs.

Filters (all 4):

threshold_filter(), fraction_filter(), boolean_filter(), consensus_backbone()

Measures (all 7):

All evaluation functions work with directed graphs.

Undirected-only methods

The following methods raise NetworkXNotImplemented when given a directed graph:

• high_salience_skeleton()

• metric_backbone()

• ultrametric_backbone()

• doubly_stochastic_filter()

• h_backbone()

• modularity_backbone()

• planar_maximally_filtered_graph()

• maximum_spanning_tree_backbone()

• glab_filter()

• sparsify()

• lspar()

• local_degree()

Behavior differences

For statistical methods on directed graphs, the p-value computation uses the out-degree and out-strength of the source node. For proximity methods on directed graphs, neighborhoods are defined using successors.

Example

import networkx as nx
import networkx_backbone as nb

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    (0, 1, 5.0), (0, 2, 3.0), (0, 3, 1.0),
    (1, 0, 2.0), (1, 2, 4.0),
    (2, 3, 6.0),
])

# Statistical methods work on directed graphs
H = nb.disparity_filter(G)
backbone = nb.threshold_filter(H, "disparity_pvalue", 0.05)